class_docstr: This class decodes each item in a batch by resizing each image
  window and save as a new image volume. Multiple output image can be
  proposed and csv output can be performed as well
class_docstr_tok: ["This","class","decodes","each","item","in","a","batch","by","resizing","each","image","window","and","save","as","a","new","image","volume",".","Multiple","output","image","can","be","proposed","and","csv","output","can","be","performed","as","well"]
docstr: Save all csv output present in the dictionary of csv_output.
  :return:
docstr_tok: ["Save","all","csv","output","present","in","the","dictionary","of","csv_output",".",":","return",":"]
code: def _save_current_csv(self):
      """
      Save all csv output present in the dictionary of csv_output.
      :return:
      """
      if self.input_image is None:
          return
      subject_name = self.reader.get_subject_id(self.image_id)
      for i in self.csv_out:
          filename = "{}_{}_{}.csv".format(
              i, subject_name, self.postfix
          )
          misc_io.save_csv_array(
              self.output_path, filename, self.csv_out[i]
          )
          self.log_inferred(subject_name, filename)
      return
  
file_path: NiftyNet/niftynet/engine/windows_aggregator_resize.py
imports: from __future__ import absolute_import, division, print_function
  import os
  from collections import OrderedDict
  import numpy
  import pandas
  import niftynet.io.misc_io
  from niftynet.engine.sampler_resize_v2 import zoom_3d
  from niftynet.engine.windows_aggregator_base import ImageWindowsAggregator
  from niftynet.layer.discrete_label_normalisation import DiscreteLabelNormalisationLayer
  from niftynet.layer.pad import PadLayer
label_desc: Use image node_id 5 for calling the ResizeSamplesAggregator obj's underlying member method code with example usage: obj._save_current_csv() without return types